Multilevel Determinants of Racial/Ethnic Disparities in Lung Cancer Screening Utilization

摘要 自2013年12月以来，美国预防服务工作组建议进行肺癌筛查 (LCS)对于有吸烟史的高危人群，低剂量计算机断层扫描， 降低肺癌死亡率的机会，特别是在种族/民族和弱势群体中， 不成比例地受到疾病的影响。然而，有人担心，LCS没有得到有效的交付， 考虑到其许多独特的执行挑战，LCS利用率仍然很低。新兴数据也 表明黑人与白色个体相比LCS吸收较差。对种族/民族的了解更少 尽管戒烟咨询是LCS的一个组成部分， 过程确定与LCS使用相关的因素，特别是那些导致种族/民族 因此，差异对于最佳地提供LCS至关重要。遵循既定的概念框架， 多层次的影响影响癌症筛查行为，我们认为LCS的利用受到以下因素的影响： 个人、社区、提供者和卫生机构层面的因素。确定多层次因素的研究 到目前为止，由于数据源中已知的限制， 可用于评估LCS，尤其是在筛选开始前的步骤。电子健康记录（EHR）是一种 公认的，但在很大程度上尚未开发的数据源，以解决LCS。与其他数据源相比， 卫生系统EHR从定义的人群中获取关于临床服务的全面纵向数据， 提供了一个强大的和有效的手段来调查LCS利用的差异的多层次的决定因素。在 我们的基础工作使用EHR数据来描述LCS利用的早期模式，我们发现了证据， 在筛选启动后的过程中存在种族/民族差异。在这一建议中，我们的目标是确定和 了解LCS使用中种族/民族差异的多层次决定因素，之前和之后 筛选启动。具体来说，我们将确定个人，邻里， 供应商和设施水平对LCS利用率的差异，从筛选的机会开始， 通过EHR记录吸烟史的完整性来衡量，以评估LCS资格（目标1）， 其次是LCS转介和启动（目标2）和转介和使用戒烟服务（目标3）。我们 我将编译、链接和分析来自社会人口统计学的可用EHR、调查问卷和地理空间数据。 从2014年到2023年，在综合卫生系统中有超过130万成年人的多样化人口。总体而言，我们 多层次分析将有助于深入了解种族/民族差异的主要和可改变的驱动因素 在LCS的关键步骤，从资格评估到筛选启动。这些发现将提供一个经验 指导卫生系统制定多层次干预措施的基础，以改善LCS的推广和利用， 既有效又公平。

ABSTRACT Since December 2013, the U.S. Preventive Services Task Force has recommended lung cancer screening (LCS) with low-dose computed tomography for high-risk individuals with a smoking history, affording a major opportunity to reduce lung cancer mortality, especially in racial/ethnic and disadvantaged populations that are disproportionately affected by the disease. Yet, there is concern that LCS is not being delivered effectively and equitably, given its many unique implementation challenges. LCS utilization remains low. Emerging data also suggest poorer uptake of LCS in Black versus white individuals. Even less is known about racial/ethnic disparities in smoking cessation, although smoking cessation counseling is an integral component of the LCS process. Identifying factors associated with LCS utilization, particularly those that contribute to racial/ethnic disparities, is thus critical to deliver LCS optimally. Following well-established conceptual frameworks in which multiple levels of influence affect cancer screening behaviors, we posit that LCS utilization is affected by individual-, neighborhood-, provider-, and health facility-level factors. Studies to identify multilevel factors associated with LCS utilization have been limited to date, due in part to known constraints in the data sources available to evaluate LCS, especially at steps before screening initiation. Electronic health records (EHR) are a recognized but largely untapped data source to address LCS. Compared to other data sources, integrated health system EHRs capture comprehensive longitudinal data on clinical services from a defined population, providing a robust and efficient means to investigate multilevel determinants of disparities in LCS utilization. In our foundational work using EHR data to characterize early patterns of LCS utilization, we found evidence of racial/ethnic disparities in the process after screening initiation. In this proposal, we aim to identify and understand multilevel determinants of racial/ethnic disparities in LCS utilization, before and up through screening initiation. Specifically, we will determine the influence of factors at the individual, neighborhood, provider, and facility levels on disparities in LCS utilization, starting from the opportunity to be screened, as measured by the completeness of EHR documentation on smoking history to assess LCS eligibility (Aim 1), followed by LCS referral and initiation (Aim 2) and referral and use of smoking cessation services (Aim 3). We will compile, link, and analyze available EHR, questionnaire, and geospatial data from a sociodemographically diverse population of over 1.3 million adults in an integrated health system from 2014 to 2023. Overall, our multilevel analysis will generate valuable insight into the major and modifiable drivers of racial/ethnic disparities at key steps in LCS from eligibility assessment to screening initiation. These findings will provide an empirical basis to guide health systems in developing multilevel interventions that improve LCS outreach and utilization, both effectively and equitably.